LADRI: LeArning-based Dynamic Risk Indicator in Automated Driving System

TL;DR

This paper presents a real-time risk assessment framework for Automated Driving Systems using Artificial Neural Networks to analyze onboard sensor data, aiming to improve safety and situational awareness.

Contribution

It introduces a novel ANN-based dynamic risk assessment method tailored for real-time analysis of ADS environment data, surpassing traditional risk evaluation techniques.

Findings

Enhanced risk detection accuracy in real-time scenarios

Improved situational awareness for autonomous vehicles

Potential reduction in accident rates

Abstract

As the horizon of intelligent transportation expands with the evolution of Automated Driving Systems (ADS), ensuring paramount safety becomes more imperative than ever. Traditional risk assessment methodologies, primarily crafted for human-driven vehicles, grapple to adequately adapt to the multifaceted, evolving environments of ADS. This paper introduces a framework for real-time Dynamic Risk Assessment (DRA) in ADS, harnessing the potency of Artificial Neural Networks (ANNs). Our proposed solution transcends these limitations, drawing upon ANNs, a cornerstone of deep learning, to meticulously analyze and categorize risk dimensions using real-time On-board Sensor (OBS) data. This learning-centric approach not only elevates the ADS's situational awareness but also enriches its understanding of immediate operational contexts. By dissecting OBS data, the system is empowered to pinpoint its current risk profile, thereby enhancing safety prospects for onboard passengers and the broader traffic ecosystem. Through this framework, we chart a direction in risk assessment, bridging the conventional voids and enhancing the proficiency of ADS. By utilizing ANNs, our methodology offers a perspective, allowing ADS to adeptly navigate and react to potential risk factors, ensuring safer and more informed autonomous journeys.